Optical microscopy is an effective tool to visualize miniature objects with high spatiotemporal resolution and in a nearly noninvasive manner. Its utility has been widespread in numerous applications including industrial, life science, and biomedical research. For instance, state-of-the-art biological cellular assay techniques involved in life science research and clinical diagnosis have adopted optical microscopy as a method of classifying different cell types and/or disease stages in order to determine their respective cellular functions.
The functional and structural information of biological cells can commonly be inferred by their correlated image contrasts. While exogenous fluorescent labels are the prevalent contrast agents used in many cell biology applications, they are not always ideal in view of the complications of introduced cytotoxicity and photobleaching as well as laborious specimen preparations. In contrast, endogenous image contrast (e.g., absorption, scattering) could serve as an effective intrinsic biomarker in certain applications without the need for a label or a stain and the associated laborious sample preparation procedures. Among different label-free optical imaging modalities, quantitative phase-contrast imaging (QPI) possesses an unique attribute of providing not only non-invasive high image contrast, but also quantitative evaluation of cellular information at nanometer scale based on the mapping of optical phase shift across transparent cells or tissues.
Information derived from QPI can then be used as intrinsic biomarkers for cellular identification and understanding of the corresponding physiological information such as the diseased state of cells and tissues. Intrinsic parameters including optical (e.g., light scattering, refractive index), physical (e.g., size, morphology) and mechanical (e.g., mass density, stiffness, deformability) properties of the biological specimens have now been recognized as the new dimensions of phenotypic information valuable for bioassays as complementary parameters to other well understood molecular-specific information. Notably, refractive index of cell nuclei can serve as a distinct indicator for label-free detection of cancer cells with high sensitivity.
However, such high-information-content measurements of innumerable cells or large-area tissues generally demand high-throughput imaging capability, which directly links to the image acquisition rate of QPI. Similar to other classical optical imaging systems, the fact that QPI mostly requires CCD/CMOS sensors for image acquisition leads to the common trade-off between imaging sensitivity and speed. Intrinsic parameters aforementioned have long been left uncharted, particularly in the context of high-throughput single-cell analysis—a popular tool today for unraveling the complex cellular physiology and thus understanding the pathogenesis of diseases by studying different types and stages of cells in their lineages down to single-cell precision. This is challenging because cellular properties, influenced by genetic diversity and/or epigenetic variations, are now known to be highly heterogeneous, even within the same cell population.
In order to evaluate cell-to-cell differences, or to detect the rare cells, the characteristics of individual cells should be cataloged. Higher-confidence characterization typically comes with a progressively larger number of parameters that can be extracted from each single-cell measurement. This drives the blooming interest in developing new approaches for realizing high-throughput and accurate single-cell analysis, which can have a profound impact on advancing drug discovery, aberrant stem cell screening, and rare cancer cell detection, among many other applications. Incorporating quantitative single-cell imaging is thus of great value in advancing single-cell analysis. In the related art, though, higher content typically comes at the expense of lower throughput, and vice versa. This is exemplified by the emerging interest in adding imaging capability to flow cytometry. By accessing the additional spatial information of the cells, these imaging flow cytometers only achieve an imaging throughput (about 1,000 cells/sec) that is orders of magnitude slower than that of non-imaging flow cytometers.
Built upon the classical phase-contrast and differential interference contrast (DIC) microscopy, many related art QPI techniques are mostly based on either interferometric or holographic approaches, which require sophisticated setups and can be vulnerable to mechanical disturbance. Even though QPI can be performed without exogenous labels, related art QPI techniques are not fully compatible with high-throughput image-based cellular assays because of their speed limitation. In addition, the image acquisition rate can be largely impeded by the fundamental speed-sensitivity trade-off in CCD/CMOS image sensors, and phase-retrieval in these techniques can often be computationally intensive, thereby hindering efficient high-speed real-time quantitative cellular image analysis.
As a high-speed imaging technology, time-stretch imaging is particularly suitable for high-throughput single-cell imaging and analysis due to its ultrafast imaging speed (orders-of-magnitude faster than classical image sensors) operated in real-time continuously. However, time-stretch imaging has been primarily compatible with bright-field imaging mode, which is not suitable to deliver high-contrast image quality as well as high-content quantitative image analysis of label-free/unstained biological cells and tissues. QPI has been combined with time-stretch imaging based on interferometry—implying again that the implementation is susceptible to mechanical disturbance and is not suitable for long-term operation.